It’s often claimed that showing real people in social media posts helps to humanize a brand and potentially garner more engagement. But can the simple act of showing a face in a post actually boost post performance?
In this month’s edition of Random Labs, we explored the impact this basic human element has on post performance across various platforms.
As we've reiterated in past experiments, modern social media platforms offer users a wealth of data on their own content, providing great opportunities to fine-tune and optimize strategies. (Check out our previous Random Labs blog on TikTok video length performance.) We will analyze the posts we have shared in 2024 so far on our own agency’s social media platforms.
In our experiment, we focused primarily on image content.
Short-form videos and Reels have become highly effective content formats across most platforms. We've observed that a large percentage of this type of content already features people within the posts themselves.
We will utilize multiple hypothesis testing across three major platforms (Facebook, Instagram, and LinkedIn) to answer a series of questions: Is there a statistically significant difference between posts with faces and those without? If so, what metrics are primarily affected?
Before we deep dive and answer these questions, we will make the following data assumptions and preparation disclaimers across all platforms:
Let’s take a look at some Random content!
Content w/ Faces | Content w/ No Faces |
| Average Engagement: 5.12 Likes: 2.52 Comments: 0.20 Shares: 0.08 Average Impressions: 38.08 | Average Engagement: 4.05 Likes: 2.50 Comments: 0.06 Shares: 0.03 Average Impressions: 32.47 |
| Hypothesis Testing Results: Engagement: No statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.402 > 0.05) Likes: No statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.323 > 0.05) Comments: No statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.245 > 0.05) Shares: No statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.195 > 0.05) Impressions: No statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.127 > 0.05) | |
| Methodology explained: In our statistical method, think of the p-value as a measure of surprise. It shows how likely your data could occur by random chance if there’s no real effect. The alpha level (usually 0.05) is the cutoff we set to decide how much surprise we're okay with. If the p-value is less than alpha, it means the result is surprising enough to believe something is happening (significant). If it’s higher, we assume it's just random chance (not significant). | |
In summary, Facebook differed significantly from the other two platforms we analyzed by showing no statistically significant differences in metrics between the two content types. Whether posts had faces or no faces on this platform, there is not enough evidence to suggest its presence has any impact on post performance. Although the averages may seem to show some differences at first glance, hypothesis testing reveals that these differences are neither justified nor consistent.
We have some external hypotheses that may potentially support these results, including the year-over-year trend of a general decline in organic engagement on the platform. During this period, Facebook has shifted its focus significantly towards an algorithm centered around advertising, and paid content has disproportionately outperformed organic content based on our observations.
Additionally, we are just one account within a single industry. These results can vary depending on factors such as objectives, follower size, and more.
Content w/ Faces | Content w/ No Faces |
| Average Engagement: 22.45 Likes: 19.94 Comments: 1.61 Average Impressions: 153.61 | Average Engagement: 12.53 Likes: 11.58 Comments: 0.28 Average Impressions: 81.88 |
| Hypothesis Testing Results: Engagement: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.006 < 0.05) Likes: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.0096 < 0.05) Comments: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.0005 < 0.05) Impressions: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.0062 < 0.05) | |
| Methodology explained: When a p-value is really close to zero, it suggests that the difference between the averages is highly unlikely to be due to random chance. In simpler terms, it means the data is showing a very strong signal that there’s a real difference between the two groups (face vs. no face content) you’re comparing. The closer the p-value is to zero, the more confident we can be that the observed difference is meaningful and not just a fluke. | |
On Instagram, our statistical tests showed significant differences in average metrics between content types. Nearly every test indicated a strong bias toward content featuring faces, with those posts consistently outperforming posts without faces, as confirmed by the statistical analyses.
This was an easy assumption considering the nature of Instagram, a highly visual platform. On Instagram, users are more drawn to emotionally engaging, personal, and relatable content, which faces provide. Faces capture attention more effectively. As additional support beyond the content we tested, the majority of high-performing Reels also tended to feature faces—this was so evident on our end that we didn’t feel the need to specifically test this content type.
Content w/ Faces | Content w/ No Faces |
| Average Engagement: 17.28 Likes: 5.44 Clicks: 11.44 Average Impressions: 121.25 | Average Engagement: 11.63 Likes: 3.70 Clicks: 7.75 Average Impressions: 94.22 |
| Hypothesis Testing Results: Engagement: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.014 < 0.05) Likes: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.012 < 0.05) Clicks: No statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.063 > 0.05) Impressions: Statistically significant difference between the averages of the two datasets at the 95% confidence level. (P-value: 0.037 < 0.05) | |
| Methodology explained: Based on the previous channels, since we generally concluded significance across Likes and Shares in tandem, we test an engagement type in clicks which is more highly prevalent on LinkedIn. | |
In terms of total engagement and impressions, we observe similar patterns on LinkedIn as on Instagram. The average overall engagement, including likes and impressions, is significantly higher for content featuring faces, with a high level of statistical confidence. Interestingly for post clicks, a highly prevalent engagement type for LinkedIn, there was no significant difference in the averages between both content types.
Clicks often represent a more intentional action, such as wanting to learn more or visit a website. Users might engage with content featuring faces by liking or viewing it without necessarily clicking through, especially if they find it visually appealing but not informative enough to warrant further action. LinkedIn users might be more likely to click on content with a strong call to action (CTA) or business-related context, which may or may not always include faces.
In summary, both Instagram and LinkedIn demonstrated strong performance across multiple metrics when content featured faces, with Instagram showing a more pronounced disparity. This raises several potential explanations, such as an algorithmic boost for this type of content, increased visual appeal, or a heightened sense of human connection.
Try testing more posts on your Instagram and LinkedIn that feature real people–whether that be your team members, customers, or clients–to start increasing your post performance.
As a reminder, results can vary across different industries and strategies, highlighting the importance of refining a strategy tailored specifically to your business needs.
Want us to bring statistically backed results for your strategy? Send us a message!
